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Multivariate coefficient of variation (MCV) charts are effective tools for monitoring process relative variability. They are developed on the assumption that the process subgroup size available for monitoring the MCV parameter is larger than the number of process characteristics. In such a case, the unbiased estimates of the in‐control mean vector and covariance matrix are used to calculate the chart monitoring statistic. Here, we study the performance of MCV control charts when only a small subgroup size is available for estimating the in‐control mean vector and covariance matrix. We examine the use of a shrinkage estimate of the covariance matrix and propose two one‐sided upward and downward least absolute shrinkage and selection operator (LASSO)‐based MCV charts for detecting upward and downward shifts in the process MCV parameter, respectively. Our simulation study shows that the LASSO‐based MCV charts outperform the classical two one‐sided MCV charts when small subgroup sizes are available for monitoring. The improved performance of the proposed LASSO‐based MCV charts in monitoring shifts in the MCV parameter is demonstrated via an illustrative case study of carbon fiber tube application, where changes are detected earlier than the classical two one‐sided MCV charts. [ABSTRACT FROM AUTHOR]

Control chart is a well‐known tool for monitoring the performance of an ongoing process. The variability of a process is an important parameter that may deteriorate the process performance if it is not taken care on time. In this study, we have proposed some new auxiliary information‐based exponentially weighted moving average (EWMA) charts for improved monitoring of process variability. We employed auxiliary information in some useful forms including ratio, regression, power ratio, ratio exponential, ratio regression, power ratio regression, and ratio exponential regression estimators. The performance of the newly developed charts is evaluated and compared with some existing charts (viz., the NEWMA, the Improved R, the Synthetic R, and the classical R charts), using some useful measures such as average run length (ARL), extra quadratic loss, and relative ARL. The comparative analysis revealed that the proposed charts outperform their counterparts, especially when there is a strong relationship between the study and the auxiliary variables. Finally, an illustrative example is provided for the monitoring of air quality data. [ABSTRACT FROM AUTHOR]

Traditionally, two isolated sequential stopping rules are employed for monitoring the time of occurrence of an event (T) and the magnitude of an event (X). Recently, several researchers recommend monitoring T and X together using some unified approach. A unified approach based on combinations of two statistics, one for monitoring T and the other for X, is often more efficient. Likewise, a new approach of simultaneous monitoring of location and scale parameters of a process, combining a max and a distance based statistics, is recently introduced in literature. Motivated by such emerging concepts, we design a new scheme combining a Max‐type and a Distance‐type schemes, referred to as the MT scheme, to monitor T and X simultaneously and efficiently. It retains the advantages of both the Max‐type and the Distance‐type schemes for joint inference. The proposed scheme is very competent in detecting a shift in the process distribution of T or X or both. Moreover, it is computationally simpler. It has nice exact expressions for design parameters. Therefore, it is easier to implement. It has a distinct advantage over its traditional counterparts in detecting moderate to large shifts. Finally, we illustrate the implementation of the proposed scheme with a real dataset of damage caused by outbreak of fire disaster. [ABSTRACT FROM AUTHOR]

Statistical process control deals with monitoring process to detect disturbances in the process. These disturbances may be from the process mean or variance. In this study, we propose some charts that are efficient for detecting early shifts in dispersion parameter, by applying the Fast Initial Response feature. Performance measures such as average run length, standard deviation of the run length, extra quadratic loss, relative average run length, and performance comparison index are used to compare the proposed charts with their existing counterparts, including the Shewhart R chart and the Shewhart S chart with and without warning lines. Others include the CUSUM R chart, the CUSUM S chart, the EWMA of ln S2, the CUSUM of ln S2, the Pσ CUSUM, the χ CUSUM, and the Change Point (CP) CUSUM charts. The proposed charts do not only detect early shifts in the process dispersion faster, but also have better overall performance than their existing counterparts. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]